A Fast Taboo Search Algorithm for the Job Shop Problem

Abstract

A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented. The algorithm is based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions. Computational experiments (up to 2,000 operations) show that the algorithm not only finds shorter makespans than the best approximation approaches but also runs in shorter time. It solves the well-known 10 \times 10 hard benchmark problem within 30 seconds on a personal computer.scheduling, heuristics, job-shop, taboo search

Combining Neighbourhoods in Fuzzy Job Shop Problems

“…To improve on efficiency, a reduced structure, denoted N 2 in the following, was proposed in [8], inspired in the proposal for the deterministic problem from [14]. The neighbourhood was based on reversing only those critical arcs at the extreme of critical blocks of a single path, so N 2 ⊂ N 1 .…”

Intelligent Decision Support and Agent-Based Techniques Applied to Wood Manufacturing

“…An example of the objective function is the one used in [58] as described in Section 3.4.1. Examples of local search methods that can be used are taboo search [24], simulated annealing and constraint optimization methods [31] [65]. It is also possible to apply swarm intelligence as a local optimization method [8] [9], such as ant colony optimization, particle swarm optimization and intelligent water drops.…”

Ejection chain and filter-and-fan methods in combinatorial optimization

“…The method considers two types of neighborhoods N 1 (Aarts et al, 1994) and N 2 (Nowichi and Smutnicki, 1996) based on classical moves that swap two adjacent operations in the critical path (i.e. the longest path in the problem graph that represents the solution).…”